Quick Start

Most of the plots are interactive, you can click or zoom to get more details ! Also don’t hesitate to click on plots, they will zoom automatically !

Loading Packages

# Loading Packages 

library(data.table)
library(lubridate)
library(tidyverse)
library(esquisse)
library(plyr)
library(ggplot2)
library(cowplot)
library(naniar) #for NA exploration
library(sp) #spatial data
library(reshape2)
library(plotly)
library(gissr)
library(leaflet)
library(leaflet.providers)
library(geosphere)
library(DT)

Those are required packages

Ex 3.4

Loading Datas and Cleaning

Loading the dataset called “LaptopSales_red.csv” given for the Homework

FALSE Classes 'data.table' and 'data.frame':    148786 obs. of  17 variables:
FALSE  $ V1                    : int  171289 38634 260048 166045 243280 118859 249957 198058 198850 267007 ...
FALSE  $ Date                  : chr  "9/20/2008 2:49" "5/30/2008 9:52" "12/10/2008 9:26" "9/15/2008 9:41" ...
FALSE  $ Configuration         : int  528 307 235 168 517 738 301 301 479 472 ...
FALSE  $ Customer.Postcode     : chr  "NW5 1SP" "N6 6BU" "CR0 2BW" "WC2H 9PS" ...
FALSE  $ Store.Postcode        : chr  "N3 1DH" "N3 1DH" "CR7 8LE" "SW1P 3AU" ...
FALSE  $ Retail.Price          : int  413 515 315 NA 580 535 455 465 600 392 ...
FALSE  $ Screen.Size..Inches.  : int  17 15 15 15 17 17 15 15 17 17 ...
FALSE  $ Battery.Life..Hours.  : int  4 6 5 5 4 6 6 6 4 4 ...
FALSE  $ RAM..GB.              : int  2 1 2 1 2 1 1 1 1 1 ...
FALSE  $ Processor.Speeds..GHz.: num  2.4 2 2.4 2 2.4 2 1.5 1.5 2.4 2.4 ...
FALSE  $ Integrated.Wireless.  : chr  "No" "Yes" "No" "Yes" ...
FALSE  $ HD.Size..GB.          : int  300 80 80 300 120 40 120 120 300 300 ...
FALSE  $ Bundled.Applications. : chr  "No" "Yes" "Yes" "No" ...
FALSE  $ customer.X            : int  528771 528281 532781 530190 537350 532498 533130 529390 533998 532498 ...
FALSE  $ customer.Y            : int  186041 187336 166444 181139 169306 168334 182489 181270 168421 168334 ...
FALSE  $ store.X               : int  525109 525109 532714 529902 528739 528739 534057 528924 528739 532714 ...
FALSE  $ store.Y               : int  190628 190628 168302 179641 173080 173080 179682 178440 173080 168302 ...
FALSE  - attr(*, ".internal.selfref")=<externalptr>

Retail Price is the only variable missing at rate of approximately 4%

a.Price Questions:

i. At What Price are the laptops actually selling ?

This barplot shows the most frequent retail prices for all stores in 2018

We can interpret this boxplot as the mean or median retail price of the 2018 Computer Dataset, click on the white sphere to get the mean !

## [1] "Last Recorded Prices are 406 USD and 530 USD on the same Day with a mean of 468 USD"

Here is given the last recorded prices for 2018

ii.Does price change with time?

Those Plot shows different aggregations levels, can be used depending on the analysis we want, thus the granularity need.

iii. Are prices consistent across retail outlets?

Each box plots belongs to a specific stores, we can see a common trend across all stores in 2018

Looking at times series, we can see that not all stores have the same time trend, but most of them do.

iv. How does price change with configuration?

FALSE `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Using an smooth approximator, we can see two differents trends, first a rapid increase in price while being at low configurations, and then the slope tend to stay constant and low, ending with a increase with highest configurations.

b.Location Questions

i. Where are the stores and customers locatd?

Enjoy looking at each stores and customers in London UK ! You can find there exact location by clicking on them !

ii. Which stores are selling the most?

The following barplots show two ways of analyzing the stores sales results: by the number of transactions or the sales revenues they each generated during 2018.

iii. How far would customers travel to buy a laptop ?

iv. How far would customers travel to buy a laptop ? - Alternative

Each Unique Customer can be found here, scroll down and see the distance they need to travel to get to their store.

c.Revenue Questions

i. How do the sales volume in each store relate to Acell’s revenues?

You can see the proportional revenues participation of each stores in 2018.

ii. How does this relationship depend on the configuration?

d.Configuration Questions

i. What are the details of each configuration? How does this relate to price?

ii. Do all stores sell all configurations?

With this multiple facets barplots, you can spot which configuration is less or not sold depending on the store.

Ex 4.1